Accurate and fast segmentation and volume estimation of the prostate gland in magnetic resonance (MR) images are necessary steps in the diagnosis, treatment, and monitoring of prostate cancer. This paper presents an algorithm for the prostate gland volume estimation based on the semi-automated segmentation of individual slices in T2-weighted MR image sequences.
FREE DAILY AND WEEKLY NEWSLETTERS OFFERED BY CONTENT OF INTEREST
Did you find this article relevant? Subscribe to UroToday-GUOncToday!
The fields of GU Oncology and Urology are advancing rapidly including new treatments, enrolling clinical trials, screening and surveillance recommendations along with updated guidelines. Join us as one of our subscribers who rely on UroToday as their must-read source for the latest news and data on drugs. Sign up today for blogs, video conversations, conference highlights and abstracts from peer-review publications by disease and condition delivered to your inbox and read on the go.
The proposed sequential registration-based segmentation (SRS) algorithm, which was inspired by the clinical workflow during medical image contouring, relies on inter-slice image registration and user interaction/correction to segment the prostate gland without the use of an anatomical atlas. It automatically generates contours for each slice using a registration algorithm, provided that the user edits and approves the marking in some previous slices. We conducted comprehensive experiments to measure the performance of the proposed algorithm using three registration methods (i. e. , rigid, affine, and nonrigid). Five radiation oncologists participated in the study where they contoured the prostate MR (T2-weighted) images of 15 patients both manually and using the SRS algorithm. Compared to the manual segmentation, on average, the SRS algorithm reduced the contouring time by 62 % (a speedup factor of 2. 64×) while maintaining the segmentation accuracy at the same level as the intra-user agreement level (i. e. , Dice similarity coefficient of 91 versus 90 %). The proposed algorithm exploits the inter-slice similarity of volumetric MR image series to achieve highly accurate results while significantly reducing the contouring time.
Journal of digital imaging. 2015 Nov 06 [Epub ahead of print]
Farzad Khalvati, Aryan Salmanpour, Shahryar Rahnamayan, Masoom A Haider, H R Tizhoosh
Department of Medical Imaging, University of Toronto, Toronto, ON, Canada. Department of Electrical, Computer and Software Engineering, University of Ontario Institute of Technology, Oshawa, ON, Canada. , Department of Electrical, Computer and Software Engineering, University of Ontario Institute of Technology, Oshawa, ON, Canada. , Department of Medical Imaging, University of Toronto, Toronto, ON, Canada. , Centre for Bioengineering and Biotechnology, University of Waterloo, Waterloo, ON, Canada.